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Record W2909860992 · doi:10.1097/der.0000000000000418

Atopic Dermatitis and Hospitalization for Mental Health Disorders in the United States

2019· article· en· W2909860992 on OpenAlexvenueno aff
Derek Y. Hsu, Ben Smith, Jonathan I. Silverberg

Bibliographic record

VenueDermatitis · 2019
Typearticle
Languageen
FieldMedicine
TopicDermatology and Skin Diseases
Canadian institutionsnot available
FundersAgency for Healthcare Research and Quality
KeywordsMedicineMood disordersLogistic regressionQuartileMental healthOdds ratioPsychiatrySchizophrenia (object-oriented programming)DemographicsDepression (economics)Bipolar disorderPediatricsMoodDemographyConfidence intervalInternal medicineAnxiety

Abstract

fetched live from OpenAlex

Little is known about mental health (MH) emergencies in atopic dermatitis (AD) and their financial burden. We sought to determine hospitalization rates and costs of MH disorders in AD patients. We analyzed data from the Nationwide Inpatient Sample from 2002 to 2012, containing a representative 20% sample of US hospitalizations. Overall, 835 AD (1.36%) and 2,434,703 non-AD (0.75%) patients had a primary admission for an MH disorder. Atopic dermatitis patients admitted for MH disorders were more likely to be younger, Asian, of black race, and in a higher income quartile and have an increasing number of chronic conditions. In multivariable logistic regression models adjusting for demographics, AD was associated with a primary admission for MH disorders in adults, including mood disorders, schizophrenia, and developmental disorders. Atopic dermatitis was not associated with a primary admission for an MH disorder in children. There were an estimated US $183,821,629 excess costs of care annually for MH disorders in inpatients with versus without AD. In conclusion, AD was associated with higher odds of hospitalization for all MH disorders and substantial excess costs of inpatient care.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.035
Threshold uncertainty score0.257

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.006
GPT teacher head0.261
Teacher spread0.255 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations33
Published2019
Admission routes1
Has abstractyes

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